Conv.py

import time
from keras.models import Sequential
from keras.layers import Dense,InputLayer,Dropout,LSTM,Conv1D,Flatten,GlobalAveragePooling1D,MaxPool1D
from keras.callbacks import TensorBoard
from keras.optimizers import Adam
from processing import build_dataset
from utils import init_session
from MLP import test
init_session()
batch_size=500
epochs_num=1
process_datas_dir="file\\process_datas.pickle"
log_dir="log\\Conv.log"
model_dir="file\\Conv_model"
def train(train_generator,train_size,input_num,dims_num):
    print("Start Train Job! ")
    start=time.time()
    inputs=InputLayer(input_shape=(input_num,dims_num),batch_size=batch_size)
    layer1=Conv1D(64,3,activation="relu")
    layer2=Conv1D(64,3,activation="relu")
    layer3=Conv1D(128,3,activation="relu")
    layer4=Conv1D(128,3,activation="relu")
    layer5=Dense(128,activation="relu")
    output=Dense(2,activation="softmax",name="Output")
    optimizer=Adam()
    model=Sequential()
    model.add(inputs)
    model.add(layer1)
    model.add(layer2)
    model.add(MaxPool1D(pool_size=2))
    model.add(Dropout(0.5))
    model.add(layer3)
    model.add(layer4)
    model.add(MaxPool1D(pool_size=2))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(layer5)
    model.add(Dropout(0.5))
    model.add(output)
    call=TensorBoard(log_dir=log_dir,write_grads=True,histogram_freq=1)
    model.compile(optimizer,loss="categorical_crossentropy",metrics=["accuracy"])
    model.fit_generator(train_generator,steps_per_epoch=train_size//batch_size,epochs=epochs_num,callbacks=[call])
#    model.fit_generator(train_generator, steps_per_epoch=5, epochs=5, callbacks=[call])
    model.save(model_dir)
    end=time.time()
    print("Over train job in %f s"%(end-start))

if __name__=="__main__":
    train_generator, test_generator, train_size, test_size, input_num, dims_num=build_dataset(batch_size)
    train(train_generator,train_size,input_num,dims_num)
    test(model_dir,test_generator,test_size,input_num,dims_num,batch_size)